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import numpy as np
import torch
from einops import rearrange
from torchvision import transforms
from tools.tools import resample_rgb
from tools.visualization import tensor2rgb
class MonocularCalibrator(torch.nn.Module):
def __init__(self, l1_th=0.02):
""" Calibrate Camera Intrinsic from Incidence Field.
Args:
l1_th (float): RANSAC Inlier Count Threshold. Default 0.02.
RANSAC_num (int): RANSAC Random Sampling Point Number. Default: 20000.
"""
super().__init__()
self.RANSAC_num = 20000
self.l1_th = l1_th
def initcoords2D(self, b, h, w, device, homogeneous=False):
""" Init Normalized Pixel Coordinate System
"""
query_coords = torch.meshgrid(
(
torch.linspace(-1 + 1 / h, 1 - 1 / h, h, device=device),
torch.linspace(-1 + 1 / w, 1 - 1 / w, w, device=device),
),
indexing='ij'
)
query_coordsx, query_coordsy = query_coords[1], query_coords[0]
if homogeneous:
query_coords = torch.stack((query_coordsx, query_coordsy, torch.ones_like(query_coordsx)), dim=0).view([1, 3, h, w]).expand([b, 3, h, w])
else:
query_coords = torch.stack((query_coordsx, query_coordsy), dim=0).view([1, 2, h, w]).expand([b, 2, h, w])
return query_coords
@staticmethod
def norm_intrinsic(intrinsic, b, h, w, device):
""" Map Intrinsic to Normalized Image Coordinate System [-1, 1]
"""
scaleM = torch.eye(3).view([1, 3, 3]).expand([b, 3, 3]).to(device)
scaleM[:, 0, 0] = float(1 / w) * 2
scaleM[:, 1, 1] = float(1 / h) * 2
scaleM[:, 0, 2] = -1.0
scaleM[:, 1, 2] = -1.0
return scaleM @ intrinsic
@staticmethod
def unnorm_intrinsic(intrinsic, b, h, w, device):
""" Unmap Intrinsic to Image Coordinate System [0.5, h / w - 0.5]
"""
scaleM = torch.eye(3).view([1, 3, 3]).expand([b, 3, 3]).to(device)
scaleM[:, 0, 0] = float(1 / w) * 2
scaleM[:, 1, 1] = float(1 / h) * 2
scaleM[:, 0, 2] = -1.0
scaleM[:, 1, 2] = -1.0
return scaleM.inverse() @ intrinsic
def intrinsic2incidence(self, intrinsic, b, h, w, device):
""" Compute Gt Incidence Field from Intrinsic
"""
coords3d = self.initcoords2D(b, h, w, device, homogeneous=True)
intrinsic = MonocularCalibrator.norm_intrinsic(intrinsic, b, h, w, device)
intrinsic = intrinsic.view([b, 1, 1, 3, 3])
coords3d = rearrange(coords3d, 'b d h w -> b h w d 1')
coords3d = torch.linalg.inv(intrinsic) @ coords3d
coords3d = rearrange(coords3d.squeeze(-1), 'b h w d -> b d h w')
normalray = torch.nn.functional.normalize(coords3d, dim=1)
return normalray
def scoring_function_xy(self, normal_RANSAC, normal_ref):
""" RANSAC Scoring Function
"""
xx, yy, _ = torch.split(normal_RANSAC, 1, dim=1)
xxref, yyref, zzref = torch.split(normal_ref, 1, dim=0)
xxref = xxref / zzref
yyref = yyref / zzref
diffx = torch.sum((xx - xxref.unsqueeze(0)).abs() < self.l1_th, dim=[1, 2])
diffy = torch.sum((yy - yyref.unsqueeze(0)).abs() < self.l1_th, dim=[1, 2])
return diffx, diffy
def get_sample_idx(self, h, w, prob=None, seed=None):
if seed is not None:
np.random.seed(seed)
if prob is not None:
prob = prob.view([1, int(h * w)]).squeeze().cpu().numpy()
sampled_index = np.random.choice(
np.arange(int(h * w)),
size=self.RANSAC_num,
replace=False,
p=prob,
)
else:
sampled_index = np.random.choice(
np.arange(int(h * w)),
size=self.RANSAC_num,
replace=False,
)
return sampled_index
def sample_wo_neighbour(self, x, sampled_index):
assert len(x) == 1
_, ch, h, w = x.shape
x = x.contiguous().view([ch, int(h * w)])
return x[:, sampled_index]
def minimal_solver(self, coords2Ds, normalrays, RANSAC_trial):
""" RANSAC Minimal Solver
"""
minimal_sample = 2
device = coords2Ds.device
sample_num = int(minimal_sample * RANSAC_trial)
coords2Dc, normal = coords2Ds[:, 0:sample_num], normalrays[:, 0:sample_num]
x1, y1, _ = torch.split(coords2Dc, 1, dim=0)
n1, n2, n3 = torch.split(normal, 1, dim=0)
n1 = n1 / n3
n2 = n2 / n3
x1, y1 = x1.view(minimal_sample, RANSAC_trial), y1.view(minimal_sample, RANSAC_trial)
n1, n2 = n1.view(minimal_sample, RANSAC_trial), n2.view(minimal_sample, RANSAC_trial)
fx = (x1[1] - x1[0]) / (n1[1] - n1[0] + 1e-10)
bx = (x1[0] - n1[0] * fx) * 0.5 + (x1[1] - n1[1] * fx) * 0.5
fy = (y1[1] - y1[0]) / (n2[1] - n2[0] + 1e-10)
by = (y1[0] - n2[0] * fy) * 0.5 + (y1[1] - n2[1] * fy) * 0.5
intrinsic = torch.eye(3).view([1, 3, 3]).repeat([len(fx), 1, 1]).to(device)
intrinsic[:, 0, 0] = fx
intrinsic[:, 1, 1] = fy
intrinsic[:, 0, 2] = bx
intrinsic[:, 1, 2] = by
return intrinsic
def calibrate_camera_4DoF(self, incidence, RANSAC_trial=2048):
""" 4DoF RANSAC Camera Calibration
Args:
incidence (tensor): Incidence Field
RANSAC_trial (int): RANSAC Iteration Number. Default: 2048.
"""
# Calibrate assume a simple pinhole camera model
b, _, h, w = incidence.shape
device = incidence.device
coords2D = self.initcoords2D(b, h, w, device, homogeneous=True)
sampled_index = self.get_sample_idx(h, w)
normalrays = self.sample_wo_neighbour(incidence, sampled_index)
coords2Ds = self.sample_wo_neighbour(coords2D, sampled_index)
# Prepare for RANSAC
intrinsic = self.minimal_solver(coords2Ds, normalrays, RANSAC_trial)
valid = (intrinsic[:, 0, 0] < 1e-2).float() + (intrinsic[:, 1, 1] < 1e-2).float()
valid = valid == 0
intrinsic = intrinsic[valid]
# RANSAC Loop
intrinsic_inv = torch.linalg.inv(intrinsic)
normalray_ransac = intrinsic_inv @ coords2Ds.unsqueeze(0)
diffx, diffy = self.scoring_function_xy(normalray_ransac, normalrays)
intrinsic_x, intrinsic_y = intrinsic, intrinsic
maxid = torch.argmax(diffx)
fx, bx = intrinsic_x[maxid, 0, 0], intrinsic_x[maxid, 0, 2]
maxid = torch.argmax(diffy)
fy, by = intrinsic_y[maxid, 1, 1], intrinsic_y[maxid, 1, 2]
intrinsic_opt = torch.eye(3).to(device)
intrinsic_opt[0, 0] = fx
intrinsic_opt[0, 2] = bx
intrinsic_opt[1, 1] = fy
intrinsic_opt[1, 2] = by
intrinsic_opt = MonocularCalibrator.unnorm_intrinsic(intrinsic_opt.unsqueeze(0), b, h, w, device)
return intrinsic_opt.squeeze(0)
def calibrate_camera_1DoF(self, incidence, r, RANSAC_trial=2048):
""" 1DoF RANSAC Camera Calibration
Args:
incidence (tensor): Incidence Field.
r: Aspect Ratio Restoration from Network Inference Resolution (480 x 640) to the Original Resolution
RANSAC_trial (int): RANSAC Iteration Number. Default: 2048.
"""
# Calibrate assume a simple pinhole camera model
b, _, h, w = incidence.shape
assert b == 1
# r = (scaleM[0, 1, 1] / scaleM[0, 0, 0]).item()
device = incidence.device
coords2D = self.initcoords2D(b, h, w, device, homogeneous=True)
sampled_index = self.get_sample_idx(h, w)
normalrays = self.sample_wo_neighbour(incidence, sampled_index)
coords2Ds = self.sample_wo_neighbour(coords2D, sampled_index)
# Prepare for RANSAC
fs = torch.linspace(100, 4096, steps=RANSAC_trial)
intrinsic = torch.eye(3).view([1, 3, 3]).expand([2048, 3, 3]).contiguous().to(device)
intrinsic[:, 0, 2] = float(w / 2)
intrinsic[:, 1, 2] = float(h / 2)
intrinsic[:, 0, 0] = fs
intrinsic[:, 1, 1] = fs * r
intrinsic = self.norm_intrinsic(intrinsic, b, h, w, device)
# RANSAC Loop
intrinsic_inv = torch.linalg.inv(intrinsic)
normalray_ransac = intrinsic_inv @ coords2Ds.unsqueeze(0)
diffx, diffy = self.scoring_function_xy(normalray_ransac, normalrays)
maxid = torch.argmax(diffx + diffy)
fx, bx = intrinsic[maxid, 0, 0], intrinsic[maxid, 0, 2]
fy, by = intrinsic[maxid, 1, 1], intrinsic[maxid, 1, 2]
intrinsic_opt = torch.eye(3).to(device)
intrinsic_opt[0, 0] = fx
intrinsic_opt[0, 2] = bx
intrinsic_opt[1, 1] = fy
intrinsic_opt[1, 2] = by
intrinsic_opt = MonocularCalibrator.unnorm_intrinsic(intrinsic_opt.unsqueeze(0), b, h, w, device)
return intrinsic_opt.squeeze(0)
def restore_image(self, image, intrinsic, fixcrop=True):
# Adjust Intrinsic with Crop and Resize
w, h = image.size
wt, ht = image.size
# Fix Aspect Ratio, Avoid Image Reduced
resizeM = np.eye(3)
if intrinsic[0, 0] > intrinsic[1, 1]:
r = intrinsic[0, 0] / intrinsic[1, 1]
resizeM[1, 1] = r
wt, ht = wt, ht * r
else:
r = intrinsic[1, 1] / intrinsic[0, 0]
resizeM[0, 0] = r
wt, ht = wt * r, ht
wt, ht = int(np.ceil(wt).item()), int(np.ceil(ht).item())
# Fix Crop
cropM = np.eye(3)
if fixcrop:
intrinsic_ = resizeM @ intrinsic
padding_lr, padding_ud = intrinsic_[0, 2] - wt / 2, intrinsic_[1, 2] - ht / 2
if padding_lr < 0:
cropM[0, 2] = -padding_lr
if padding_ud < 0:
cropM[1, 2] = -padding_ud
wt, ht = int(np.ceil(wt + np.abs(padding_lr)).item()), int(np.ceil(ht + np.abs(padding_ud)).item())
resample_matrix = np.linalg.inv(cropM @ resizeM)
totensor = transforms.ToTensor()
image_restore = resample_rgb(
totensor(image).unsqueeze(0),
torch.from_numpy(resample_matrix).float().view([1, 3, 3]),
batch=1, ht=ht, wd=wt, device=torch.device("cpu")
)
return tensor2rgb(image_restore, viewind=0) |